We adopt MAML as the meta learning algorithm.
The goal of MAML is to find a sensitive initial point of model parameters through cross-task meta-training such that a few gradient updates lead to large performance improvement in a new task during meta-test.
Previous works have shown that taking model parameters pre-trained on large-scale existing data as a warm start of meta-training can achieve better performance [1]}, i.e., transferring knowledge from large-scale pre-training.
Therefore, in our case, the training process of Meta-Voice consists of three phases: large-scale pre-training, meta learning and adaptation to new speakers, as illustrated in Fig. REF .
